Pan-Cancer Project discovers causes of previously unexplained cancers, pinpoints cancer-causing events and zeroes in on mechanisms of development

Toronto – (February 5, 2020) An international team has completed the most
comprehensive study of whole cancer genomes to date, significantly improving
our fundamental understanding of cancer and signposting new directions for its
diagnosis and treatment.

The
ICGC/TCGA Pan-Cancer Analysis of
Whole Genomes Project (PCAWG), known as the Pan-Cancer Project, a collaboration involving more than 1,300 scientists and clinicians from 37 countries, analyzed
more than 2,600 genomes of 38 different tumour types, creating a huge resource of
primary cancer genomes. This was then the launch-point for 16 working groups
studying multiple aspects of cancer’s development, causation, progression and
classification.

Previous
studies focused on the 1 per cent of the genome that codes for proteins,
analogous to mapping the coasts of the continents. The Pan-Cancer Project
explored in considerably greater detail the remaining 99 per cent of the
genome, including key regions that control switching genes on and off —
analogous to mapping the interiors of continents versus just their coastlines.

The Pan-Cancer
Project has made available a comprehensive resource for cancer genomics
research, including the raw genome sequencing data, software for cancer
genome analysis, and multiple interactive websites exploring various aspects of
the Pan-Cancer Project data.

The
Pan-Cancer Project extended and advanced methods for analyzing cancer genomes which included cloud computing, and by applying these methods to its large dataset,
discovered new knowledge about cancer biology and confirmed important findings
of previous studies. In 23 papers
published today in Nature and its affiliated journals, the Pan-Cancer
Project reports that:

The cancer genome is finite and knowable, but enormously complicated. By combining sequencing of the whole cancer genome with a suite of analysis tools, we can characterize every genetic change found in a cancer, all the processes that have generated those mutations, and even the order of key events during a cancer’s life history.

Researchers are close to cataloguing all of the biological pathways involved in cancer and having a fuller picture of their actions in the genome. At least one causal mutation was found in virtually all of the cancers analyzed and the processes that generate mutations were found to be hugely diverse — from changes in single DNA letters to the reorganization of whole chromosomes. Multiple novel regions of the genome controlling how genes switch on and off were identified as targets of cancer-causing mutations.

Through a new method of “carbon dating,” Pan-Cancer researchers discovered that it is possible to identify mutations which occurred years, sometimes even decades, before the tumour appears. This opens, theoretically, a window of opportunity for early cancer detection.

Tumour types can be identified accurately according to the patterns of genetic changes seen throughout the genome, potentially aiding the diagnosis of a patient’s cancer where conventional clinical tests could not identify its type. Knowledge of the exact tumour type could also help tailor treatments.

“The incredible
work of the Pan-Cancer Project team that was unveiled today is the culmination
of a remarkable international collaboration that has enriched our understanding
and provided new ways to approach the prevention, diagnosis and treatment of
cancer,” said The Honourable Ross Romano, Ontario’s Minister of Colleges and
Universities. “I congratulate the entire research group on this ground-breaking
achievement in cancer research. Ontarians can be proud of the leading role OICR
played in this initiative.”

“The findings we have shared with the world
today are the culmination of an unparalleled, decade-long collaboration that
explored the entire cancer genome,” says Dr. Lincoln Stein, member of the Project
steering committee and Head of Adaptive Oncology at the Ontario Institute for
Cancer Research (OICR). “With the knowledge we have gained about the origins
and evolution of tumours, we can develop new tools to detect cancer earlier,
develop more targeted therapies and treat patients more successfully.”

“The
Pan-Cancer Project has generated a much-needed deeper understanding of the
biology of cancer and how the elusive and untapped “dark matter” in the human
genome drives cancer,” says Dr. Laszlo Radvanyi, OICR’s President and
Scientific Director. “These discoveries can lead to totally new area of targets
for cancer therapy. It is gratifying to know that OICR helped to lead
the international effort, while also integrating a collaborative
network of Ontario researchers to play a leading role in this global project.
It is a further indication of the value of our strategic investments into data
infrastructure, research and informatics expertise, as well as the
value the Ontario government continues to create in supporting OICR. I
congratulate Dr. Stein, his team and all Pan-Cancer researchers on this landmark
achievement.”

OICR is a
collaborative, not-for-profit research institute funded by the Government of
Ontario. We conduct and enable high-impact translational cancer research to
accelerate the development of discoveries for patients around the world while
maximizing the economic benefit of this research for the people of Ontario. For
more information visit www.oicr.on.ca.

Three billion letters of code make up our complete genetic
blueprint, yet everything we know about cancer to date comes from only one per
cent of those letters.

What about the other 99 per cent? Could those regions be holding
clues to new cancer solutions and cures? What could we find if we looked into
this dark matter? Dr. Lincoln Stein wanted to find out – and he wasn’t alone.

In the fall of 2015, more than 1,300 investigators from the International Cancer Genome Consortium (ICGC) expressed interest in exploring these uncharted regions. Four years and hundreds of terabytes of data analysis later, they’ve found ways to map the evolutionary history of cancer, identified traces of the disease long before it is diagnosed, and elevated the world’s standards for genomics data sharing and research.

A collective goal, a
collaborative feat

Jennifer Jennings

“When this project was first announced, we were delighted by the
overwhelming interest,” says Jennifer Jennings, Senior Project Manager of the
ICGC. She says that was when the scientific leadership of ICGC realized that a
concerted effort was needed to address common computational and logistical
challenges, leverage the strengths of collaborators and develop shared
infrastructure to achieve the ultimate goals of this research.

Stein and a small group of scientific leaders took on the
challenge of synchronizing research groups with similar research goals,
strategically rearranging expertise and coordinating collaboration on an
international scale.

“Organizing and bringing these researchers together was the
greatest challenge,” says Stein, who is the Head of Adaptive Oncology at OICR.
“Working with others may be slower at first and the benefits aren’t always
evident, but the rigour of the resulting science and the progress made is
greater than what any of us could do on our own.”

Turning data into discoveries

Dr.Lincoln Stein

PCAWG researchers went on to investigate more than 2,600 cancer
whole genomes from ICGC patient donors across more than 20 primary disease
sites such as the pancreas and the brain. They created the computational tools
and established the necessary infrastructure to process and analyze more than
800 terabytes of genomic data in a standardized, accurate and timely fashion.

“For exceptional cases like in certain ovarian cancers, we were
able to see these early events happening 10 to 20 years before the patient has
any symptoms,” says Stein. “This opens up a much larger window of opportunity
for earlier detection and treatment than we thought possible.”

Understanding the order of genetic changes that lead to cancer –
or the probability that one will occur after another – may allow researchers to
outsmart how a tumour evolves. This knowledge could help devise new strategies
to treat these changes as they occur or prevent them from occurring in the
first place, Stein says.

PCAWG researchers have also discovered common patterns in the distribution of genetic mutations that may point to new causes of cancer. Similar to the common genetic signatures associated with smoking and ultraviolet radiation, these patterns may point to unknown environmental or behavioural causes that, once fully understood, could be used to change course and help prevent cancer.

“The biological insights discovered through PCAWG have
tremendously advanced our understanding of cancer genomics and we’re
approaching a place where we know all the molecular pathways involved with
cancer,” says Stein. “We’ve discovered the causes of two thirds of cancers that
were previously unexplained — but this is just the beginning.”

Setting new standards for the future

Last July, PCAWG data were officially made available for the scientific community to use as a resource for future cancer research. The key PCAWG findings were recently published in a collection of more than 20 scholarly papers in Nature and its affiliated journals. An expected 40 additional papers relying on PCAWG data will be published within the next year alone.

PCAWG methodologies are now the world’s gold standard for whole genome data processing and analysis. They will continue to be used for years to come as more patient samples are collected and sequenced around the world. All related computational tools, including the data exploration and discovery tools, have been made publicly available.

“We made both the genomic data, and the computational pipelines to analyze it, free to use for the global cancer research community,” says Stein. “Now, others can analyze these data – or new data – at the same level as we have in the pursuit of new cancer research discoveries.”

Pan-Cancer Project researchers develop deep learning system that can determine where a cancer originates with better accuracy than human experts

If doctors know where a patient’s cancer
started, they can better treat the disease. Unfortunately, this is not always possible,
but AI could play a role in solving that.

In a study published today in Nature Communications, a Toronto-based researcher group developed a deep learning system that can accurately classify cancers and identify where they originated based on patterns in their DNA. The system could potentially help clinicians differentiate difficult-to-classify tumours and help recommend the most appropriate treatment option for their patients.

“We reasoned that there was something within
the cancer’s DNA that could help us classify these tumours,” says Dr. Quaid
Morris, OICR Senior Investigator and co-lead author of the study1. “But
I didn’t expect our system to work at well as it does – in some cases, far better
than pathologists.”

The
team

Dr. Lincoln Stein, Head, Adaptive Oncology at
OICR and member of the Pan-Cancer Project Steering Committee, and his team began
to work with these data to identify patterns in a cancer’s genetic material
that could help classify these tumours. To them, this was a perfect problem for
AI.

When we started to collaborate, We realized we had something amazing. – Wei Jiao

“Deep learning models excel when they’re
trained on large amounts of data,” says Wei Jiao, Research Associate in the
Stein Lab and co-first author of the study. “We had an incredibly large dataset
to work with, the most comprehensive dataset of whole cancer genomes to date, but
we also needed the machine learning expertise.”

The Stein Lab posted their progress on bioRxiv,
an open-access repository for biology publications that have not yet been
peer-reviewed, which in turn sparked the collaboration between his team and the
Morris Lab – a group with deep machine learning expertise.

The system

The development of their deep learning system
was not simple. They mined through terabytes of data looking for patterns in
the type of mutations, the source of mutations and where mutations occurred in
the genome, among other factors.

To their surprise, they found that patterns in
driver mutations – the changes in DNA that are thought to ‘drive’ the
development of cancer – were not useful in determining where the tumour
originated. Instead, they found that patterns in the distribution of mutations
and the type of mutation within a patient’s sample could better classify the
patient’s disease.

“We knew that we could
distinguish between two different types of healthy cells by looking at how the
DNA within the cell types are packaged,” says Stein, who is a co-lead
author of the study. “We were surprised and gratified that we could do the
same using cancer cells.”

“We saw that the tightly-packaged sections –
also known as the closed chromatin – would have many more mutations than the loosely
wound sections,” says Gurnit Atwal, PhD Candidate in the Morris Lab and
co-first author of the study. “It was like the normal cell was casting a shadow
on the cancer cell, and we just had to read the shadows.”

To achieve the highest accuracy, the research
group developed a deep learning neural network-based system, a type of system
that is loosely modeled after the human brain and commonly used to recognize
patterns in images, audio and text. Their system achieved an accuracy of 91 per
cent – roughly double the accuracy that trained pathologists can achieve using
traditional methods when presented with a primary tumour and no clinical
information.

Further, they tested their model on an
additional 2,000 tumours from patients in the Netherlands who donated their
cancer genomic data to the Hartwig Medical Foundation and the system still
performed with a remarkably high level of accuracy.

“As more
cancer genomes are sequenced, we can gain the ability to classify rarer
cancers,” says Atwal. “Where we are now is great, but there is more work to be
done.”

The
potential

This study presents a deep learning system that
could potentially improve how cancers are classified, enhancing the accuracy of
current diagnostic tests and the treatment decisions they inform.

For some patients, this system could tell them
where their cancer began, giving them valuable information about which course
of treatment to choose. The system also could serve as a tool to help doctors
identify whether a tumour in a patient who has been treated for cancer in the
past is an entirely new tumour or a recurring tumour that has spread.

“A treatment plan for a cancer that originated
in the throat may be very different than one for that originated in the breast,
and the treatment for a cancer that has returned is different than for one that
has metastasized,” says Atwal. “One day, our tool could help give doctors the
power to distinguish these classes of tumours, giving patients valuable
information that wouldn’t have been available otherwise.”

The authors of the study suggest that their
system could start helping patients soon. They plan to further refine their
system for patients with rare cancers before moving towards clinical
studies.

“The potential impact of the system we’ve
developed is encouraging,” says Morris. “We look forward to turning this system
into a tool that can help clinicians and future cancer patients tackle this
disease.”

1Morris
is also a Canada CIFAR AI Chair, Faculty Member at the Vector Institute, and
Professor at the University of Toronto’s Donnelly Centre for Cellular and
Biomolecular Research.

OICR researchers scan more than 2,600 whole cancer genomes for traces of known and potentially unknown cancer-causing viruses, identifying new ways that these pathogens may eventually lead to the disease

It is estimated that viruses cause nearly 10
per cent of all cancers. These cancer-causing viruses – also known as
oncoviruses – can make changes to normal cells that may eventually lead to the
disease. As researchers better understand how oncoviruses cause cancer, they
can develop new therapies and vaccines to prevent them from doing so.

In the most extensive exploration of cancer
genomes to date, OICR researchers and collaborators discovered new insights
into the mechanisms behind the seven known oncoviruses, and provided strong
evidence that there are no other human cancer-causing viruses in existence.

Their study was published today in Nature Genetics, alongside more than 20 related publications from the Pan-Cancer Analysis of Whole Genomes Project, also known as the Pan-Cancer Project or PCAWG. The research group analyzed whole genome data from more than 2,600 patient tumours representing 35 different tumour types.

“The Pan-Cancer Project is one of the largest
cancer genome projects to date,” says Dr. Ivan Borozan, Scientific Associate at
OICR and leading co-author of the study. “This project allowed us to search for
viruses in the most comprehensive collection of cancer genomes using the latest
and most advanced techniques. To analyze this extensive dataset, we first had
to develop computational tools and analysis pipelines that can efficiently
process large-scale sequencing data and – at the same time – extract accurate
information about minute amounts of the viral genome present in each individual
sample. The results generated using these tools were then integrated to
decipher molecular mechanisms that lead to the development of cancer.”

Our research points towards a future where these cancers can be treated more effectively, and potentially prevented in the first place.– Dr. Ivan Borozan

The group discovered that an individual’s
immune system, while trying to protect itself from a certain strain of the
well-known human papillomavirus (HPV), may cause damage to normal DNA that lead
to the development of bladder, head, neck and cervical cancers.

The study also found that the hepatitis B virus
(HBV), which is linked to some liver cancers, causes damage in normal cells by
integrating into human DNA close to TERT, a well-understood cancer-driving
gene.

Spinoffs of this research initiative have led
to important discoveries about the Epstein-Barr Virus (EBV) and how it can
promote the development of stomach cancer.

“These findings can help us develop new
vaccines or therapies that target these mechanisms,” says Borozan. “Our
research points towards a future where these cancers can be treated more
effectively, and potentially prevented in the first place.”

As new sequencing research initiatives emerge, the research group’s computational tools and pipelines – which are available for the research community to use – will help further explain the mechanisms behind this complex disease.

OICR’s Dr. Shimin Shuai and Pan-Cancer Project collaborators identify new cancer-causing mutations in the non-coding region of the cancer genome

Cancer begins with a ‘driver’ mutation – a DNA abnormality that may cause mutations to accumulate and give rise to the disease. These mutations are key targets for cancer therapies but most research to date has focused on the driver mutations within a small portion of the genome – the one per cent of our DNA that codes for proteins.

In their paper, published today in Nature, the research team detailed a new set of potential driver mutations within the vast non-coding regions of the human genome. These driver mutations could point to new therapeutic approaches or new ways to personalize cancer treatment decisions in the future. The group’s analysis confirms previously reported drivers and raises doubts about others.

It’s amazing that we can use computational tools and algorithms to find important clues that direct us towards a future where precision medicine is a reality.– Dr. Shimin Shuai

“We
looked into the whole genomes of nearly 2,600 patients and some samples had tens
of thousands of mutations,” says Dr. Shimin Shuai, leader of OICR’s contribution
to the Pan-Cancer Project driver working group. “Driver mutations are really rare
in the non-coding regions of the genome so we needed to design computational
tools to find a needle in a haystack.”

A key tool behind these discoveries was a computational algorithm called DriverPower, developed by Shuai under the supervision of Dr. Lincoln Stein, Head of Adaptive Oncology at OICR. DriverPower, as described in a complementary publication in Nature Communications, can help differentiate driver mutations from other ‘passenger’ mutations across whole genomes.

“We
now have a remarkably powerful computational tool for future driver discovery,”
says Shuai, who is the first author of the Nature
Communications publication. “It’s amazing that we can use computational
tools and algorithms to find important clues that direct us towards a future
where precision medicine is a reality.”

DriverPower
identified nearly 100 potential driver mutations which will be evaluated in future
studies. As more whole genome sequencing data are collected in the future,
DriverPower will continue to be used for driver discovery.

“The
findings we have shared with the world today are the culmination of an
unparalleled, decade-long collaboration that explored the entire cancer genome,”
says Stein. “With the knowledge we
have gained about the origins and evolution of tumours, we can develop new
tools and therapies to detect cancer earlier, develop more targeted therapies
and treat patients more successfully.”

This work was part of the Pan-cancer Analysis of Whole Genomes Project (known as the Pan-Cancer Project or PCAWG), which was led in part by OICR.

Toronto-based machine learning experts map the changes that lead to cancer, revealing opportunities for earlier diagnosis and new approaches to outmaneuver the disease

A tumour is often
made up of different cells, some of which have changed – or evolved – over time
and gained the ability to grow faster, survive longer and potentially avoid
treatment. These cells, which have an ‘evolutionary advantage’, are thought to
cause the vast majority of cancer deaths but researchers now have a new tool to
tackle tumour evolution: TrackSig.

TrackSig – which
was developed by Dr. Quaid Morris and his team at the University of Toronto,
the Vector Institute and OICR – is a novel computational method that can map a
cancer’s evolutionary history from a single patient sample and in turn help
researchers thwart the disease’s next move.

“We combined
sequencing with evolutionary theory and mathematical modeling to understand how
cancers develop and adapt to resist treatment,” says Yulia Rubanova, PhD
Candidate in the Morris Lab and lead author of the study. “This understanding
lays the foundation for us to be able to predict – and impede – tumour
evolution in future cancer patients.”

This understanding lays the foundation for us to be able to predict – and impede – tumour evolution in future cancer patients– Yulia Rubanova

Previous
tumour evolution studies focused on identifying the most frequent changes – or
mutations – in a patient sample, where the most common mutations represent
changes that came earlier in the tumour’s development and less common mutations
represent more recent changes. Instead, Morris’ TrackSig charts different types
of mutations over time, generating maps of a tumour’s evolutionary history in
finer detail and with better accuracy than ever before.

“For exceptional
cases like in certain ovarian cancers, we were able to see these early events
happening 10 to 20 years before the patient has any symptoms,” says Dr. Lincoln
Stein, Head of Adaptive Oncology at OICR and member of the Pan-Cancer Project
Steering Committee. “This opens up a much larger window of opportunity for
earlier detection and treatment than we thought possible.”

The tools and findings from the Pan-Cancer Project are changing the way we think about cancer– Dr. Quaid Morris

With their new
detailed maps of tumour evolution, the research group plans to further
investigate novel cancer treatment strategies and design new therapies that can
better anticipate, prevent and overcome evolution and drug resistance.

“The tools and
findings from the Pan-Cancer Project are changing the way we think about
cancer,” says Morris. “We’ve uncovered new opportunities to improve diagnosis
and treatment, and we’ll continue to strive towards getting the best treatment
to patients at the right time.”

We sat down with Dr. Philip Awadalla, OICR
investigator and National Scientific Director of the Canadian Partnership for
Tomorrow Project, and Dr. Fabien Lamaze, Postdoctoral Fellow in the Awadalla
Lab, to discuss.

What
can RNA show us about cancer?

PA: Cancer
is thought to be a disease of the genome, where changes – or mutations – in an
individual’s DNA accumulate and eventually lead to the development of the
disease. Often, we can identify the mutations that drive this development,
figure out the related mechanisms and design new therapies with that
information, but sometimes no such ‘driver mutation’ exists.

We believe that RNA can help us unravel the
story behind these cancers that we can’t yet explain.

What
did the study find?

Dr. Fabien Lamaze

FL: In
this study, we took a deep dive into the transcriptome – the RNA – of nearly
two thousand tumour samples donated by patients from around the world,
representing 27 different types of tumours. The group found more than 1.5
million different RNA alterations and related mechanisms in these samples,
exposing the true complexity of the disease.

Interestingly, the study found key RNA
alterations in patient samples with no DNA driver mutation. This suggests that
some of the cellular changes that lead to cancer may manifest in RNA rather
than DNA mutations.

What
does this mean for the future of cancer research?

PA: We see that cancer is complex and we need even more data to fully
understand it, but we’ve also shown that we can make this happen by working
together.

FL: The Pan-Cancer Analysis of Whole Genomes Project was the product
of an enormous international study that was only made possible by the
dedication and true collaboration between thousands of researchers from around
the world. For this study, in particular, I’d like to recognize the scientific
leadership of Dr. Angela Brooks and collaborators from the University of
California, Santa Cruz.

PA: As more patient samples are collected and sequenced, we look
forward to using the software tools and infrastructure from the Pan-Cancer
Project to gain further insights into cancer biology.

How
can this help cancer patients?

FL: Understanding
the changes that lead to cancer can help us design better tests and new
treatments for future cancer patients. This study, for example, discovered six
interesting gene fusions involved with cancer, where two genes come together,
join in an abnormal way and wreak havoc. In the future, we could potentially
develop new drugs that target the downstream products of these fusions and stop
them from causing further damage in the cell.

PA: With the knowledge we’ve gained in this study, we look forward to furthering diagnostic and therapeutic research and development so we can ultimately treat patients more successfully. Work is already underway to make this happen.

What works in a lab
experiment doesn’t always work in the complex human body. But as technology
advances, researchers are gaining the ability to study different features of a
cancer cell and the interactions, mechanisms and pathways between them. As more
data become available, however, it is becoming increasingly
difficult to find the most important molecular pathways that, when blocked, can
stop the progression of the disease.

Dr. Jüri Reimand’s lab specializes in this area.

“Researchers often collect molecular data on one
aspect of a cancer cell at a time, like its DNA, RNA or proteins,” says
Reimand, who is an OICR Investigator. “If we can weave these complex molecular
datasets together into a bigger picture, we can gain a more thorough understanding
of cancer and potentially find new ways to tackle the driving mechanisms behind
the disease.”

To help interpret these data, the Reimand Lab developed ActivePathways – a statistical method that can discover significant pathways across multiple molecular omics datasets. These methods, published today in Nature Communications, allow researchers to characterize the cell at a systems-level, decipher how the components interact and tease out the most important pathways.

“We designed a simplified approach to tackle
one of the largest cancer genomics datasets to date,” says Reimand. “With these
methods we can now chart important interactions that we wouldn’t have
recognized by looking at one component or dataset alone.”

Their findings showed that cancer cells often
have related or coordinated mutations in the coding regions and the non-coding
regions of the genome.

Now, we have better methods and stronger evidence to move forward as we investigate how to block these pathways, and further, block the progression of the disease.– Dr. Jüri Reimand

“Together, we came to a consensus list of
frequently mutated molecular pathways, processes and target genes,” says
Reimand. “Now, we have better methods and stronger evidence to move forward as
we investigate how to block these pathways, and further, block the progression
of the disease.”

All tools, methods and data related to the
collaboration are freely available for the research community to use for future
research.

“We’re proud of this progress,” says Reimand.
“We look forward to the future research that will build on these findings
towards better cancer diagnostic tests and treatment options.”

OICR researchers identify novel causes of cancer
progression in the non-coding genome, opening new lines of investigation for
several cancer types

Toronto – (January 20, 2020) In an unprecedented pan-cancer analysis of whole genomes, researchers at the Ontario Institute for Cancer Research (OICR) have discovered new regions of non-coding DNA that, when altered, may lead to cancer growth and progression.

The study, recently published in Molecular Cell, reveals novel mechanisms of disease progression that could lead to new avenues of research and ultimately to better diagnostic tests and precision therapies.

Although previous
studies have focused on the two per cent of the genome that codes for proteins,
known as genes, this study analyzed mutation patterns within the vast
non-coding regions of human DNA that control how and when genes are activated.

We found evidence of new molecular mechanisms that may cause cancer and give rise to more-aggressive tumours.

“Cancer-driver
mutations are relatively rare in these large non-coding regions that often lie
far from genes, presenting major challenges for systematic data analysis,” says
Dr. Jüri Reimand, investigator at OICR and lead author of the study. “Powered
by novel statistical tools and whole genome sequencing data from more than 1,800
patients, we found evidence of new molecular mechanisms that may cause cancer
and give rise to more-aggressive tumours.”

The research group analyzed more than 100,000 sections
of each patient’s genome, focusing on the often-overlooked non-coding regions
that interact with genes through the three-dimensional
genome. One of the 30 key regions discovered was predicted
to have a significant role in regulating a known anti-tumour gene in cancer
cells, despite being more than 250,000 base pairs away from the gene in the
genome. The group performed CRISPR-Cas9 genome editing and functional
experiments in human cell lines to explore the cancer-driving properties of
this non-coding region.

“We characterized several non-coding regions
potentially involved in oncogenesis, but we’ve just scratched the surface,”
says Reimand. “With our algorithms and the rapidly growing datasets of patient
cancer genomes and epigenetic profiles, we look forward to enabling future discoveries
that could lead to new ways to predict how a patient’s cancer will progress and
ultimately new ways to target a patient’s disease or diagnose it more precisely.”

Reimand’s research group developed the
statistical methods behind this study and made them freely available for the
research community to use. These methods have been rigorously tested against
other algorithms from around the world.

We’ve shown that our method, called ActiveDriverWGS, can excavate these regions and pinpoint specific areas that are important to cancer growth.

“Looking into the non-coding genome is really
important because these vast sections regulate our genes and can switch them on
and off. Mutations in these regions can cause these regulatory switches to act
abnormally and potentially cause – or advance – cancer,” says Helen Zhu,
student at OICR and co-first author of the study. “We’ve shown that our method,
called ActiveDriverWGS, can excavate these regions and pinpoint specific areas
that are important to cancer growth.”

“Although these candidate driver mutations are
rare, we now have the first experimental evidence that one of the mutated
regions regulates cancer genes and pathways in human cell lines,” says Dr. Liis
Uusküla-Reimand, Research Associate at The Hospital for Sick Children
(SickKids) and co-first author of the study. “As the research community
collects more data, we plan to look deeper into these regions to understand how
the mutations alter gene regulation and chromatin architecture in specific
cancer types to enable the development of new precision therapies to patients
with these diseases.”

This study was supported by OICR through
funding provided by the Government of Ontario, and by the Canadian Institutes
of Health Research (CIHR), the Cancer Research Society (CRS), the Estonian
Research Council, and the Natural Sciences and Engineering Research Council of
Canada (NSERC).

Whole genome sequencing data used in this study was made available by the International Cancer Genome Consortium’s Pan-cancer Analysis of Whole Genomes Project (ICGC PCAWG), also known as the PCAWG Project or the Pan-Cancer Project.

Dr. Trevor Pugh, OICR’s Director of Genomics
and Senior Investigator, has been named one of ten winners of the 2019 TD Ready
Challenge.

The award, which is valued at $1 million, will support Pugh’s research
over the next three years as he and collaborators, including Dr. Raymond Kim at
the Princess Margaret Cancer Centre, develop an effective blood test for early
cancer detection. The test will aim to help those with hereditary cancer
syndrome, including individuals with Lynch Syndrome and people that carry
BRCA1/2 mutations.

“People who carry genetic changes that place them at a high risk of
cancer often face significant health, travel and financial burdens,” says Pugh.
“Not all surveillance tests are readily accessible in remote or lower-income
regions, so many of these people do not undergo necessary proactive
preventative screening. We want to help fix that.”

With TD’s funding, Pugh, Kim, and collaborators across Canada will work
to create an accessible blood-based screening test that can detect cancers
earlier than current methods, and guide more personalized management of
individuals at high risk of developing the disease.

“This project hinges
on close collaboration and coordination with patients and clinical teams caring
for them,” says Pugh. “TD’s support will further amplify the impact of our
work, especially that of our team’s clinical lead, Dr. Kim, as he mobilizes
hereditary genetics clinics for the benefit of patients across Canada.”

“TD’s support will allow our Ontario scientists to build on their leadership
in early cancer detection and screening,” says Dr. Laszlo Radvanyi, President
and Scientific Director of OICR. “We would like to thank TD for having the
vision to support such an important project that will positively impact the
health of Canadians. We would also like to congratulate Dr. Pugh and his team,
and look forward to their continued progress in making cancer screening more
accessible.”

As part of TD’s $1 billion commitment to community giving, the 2019 TD Ready Challenge encouraged organizations across North
America to create innovative solutions
that help increase equitable health outcomes and focus on preventative efforts.
In total, TD awarded $10 million for the 2019 Challenge to deliver
innovative healthcare solutions to those that need it most.

“OICR has brought forward a creative and
scalable solution to help increase equitable health outcomes for underserved
and remote communities,” says Andrea Barrack, Global Head, Sustainability and Corporate Citizenship, TD Bank Group. “Being a winner of the TD
Ready Challenge is a testament to the skill, ingenuity, and vision of its
creators, as well as their dedication to improving the health of their
communities and opening doors to a more inclusive tomorrow.”

A full list of The 2019 Ready Challenge
winners as well as more information about the challenge can be found at www.td.com/thereadychallenge.

Paul Tang, Computational Biologist, and Dr. Philip Zuzarte, Scientific Associate pose for a photo at OICR headquarters. Tang and Zuzarte were central to OICR’s contributions to the study.

International research group unlocks the promise of
nanopore native RNA sequencing

Studying RNA may offer
new answers to cancer – and the tools to read RNA directly are now in our
hands.

An international research
consortium, led in part by Dr. Jared Simpson at OICR, has developed new
laboratory protocols and a suite of software tools that will allow the research
community to exploit the promise of direct RNA sequencing.

Dr. Jared Simpson, OICR Investigator.

These techniques, published recently in Nature Methods, represent the first
large-scale exploration of human RNA using nanopore sequencers – the advanced handheld
sequencing devices that can read long strands of RNA.

“Unlike traditional
sequencing devices that read copies of RNA strands that are cut into little
pieces, nanopore sequencing allows us to study long strands of RNA directly
without losing important information in the copying and cutting process,” says
Paul Tang, Computational Biologist at OICR and co-first author of the
publication. “Our methods combine the power of reading RNA directly with the
power of long-read sequencing, enabling an entirely novel way to study cancer
biology.”

In collaboration with
researchers at Johns Hopkins University and the University of California Santa
Cruz, Tang and Simpson developed the software methods that could decode the
output data from a nanopore sequencer. Their methods used a machine learning
technique, called a Hidden Markov Model, to determine the letters of code
within an RNA strand.

“With these methods,
we’ve shown that you can leverage nanopore RNA sequencing to gain a lot of
valuable information that we couldn’t have otherwise,” Tang says. “We’re very
happy to see this work published because we are enabling others to study a new
aspect of cancer biology and we look forward to the research discoveries to
come.”

These new methods have been integrated into Simpson’s already-popular nanopolish software suite which is routinely used by the nanopore community around the world.

Kathleen Houlahan, first author of the study and a PhD candidate at OICR.

International
study, led by researchers at OICR, takes a deep dive into how prostate cancer
is inherited and points to new opportunities for improved screening,
monitoring, treatment and prevention

Prostate cancer is one of the most common
cancers in men, but remains one of the most difficult to prevent and a
challenge to treat. Some DNA mutations that lead to prostate cancer are
inherited yet some collect over a lifetime. Understanding how these mutations
interact and contribute to the disease could help patients and their doctors
better manage the disease.

In a study, published today in Nature Medicine, Kathleen Houlahan et al. take a deep dive into the inherited factors driving prostate cancer and how these factors affect the course of the disease at a cellular level.

“Prostate cancer is thought to be, in part, an
inherited disease,” says Houlahan, first author of the study and a PhD candidate
at OICR. “The DNA that a man is born with has an effect on whether he will
develop prostate cancer and how aggressive the cancer will be. We set out to
uncover how this happens.”

The study investigated the connection between
inherited mutations – also known as germline mutations – and a range of
important DNA-regulating processes, like DNA methylation.

The associations found in the study, Houlahan
says, are a resource that can help bridge our gap in understanding between
germline mutations and the mutations that men acquire over their lifetime that
eventually lead to prostate cancer.

“When we understand how inherited mutations work,
patients with these mutations can be screened and monitored more effectively to
ensure the patient is receiving the most appropriate treatment and avoiding
unnecessary side effects,” says Houlahan. “We’ve seen this work for patients
with mutations in the BRCA genes, but we still need more personalized options
for the many men who are living with prostate cancer.”

Since germline mutations can be inherited and
are present in nearly all cells in a man’s body, this research demonstrates the
possibility of using non-invasive blood-based tests, rather than invasive
tumour biopsies, to monitor prostate cancers.

“We could use these findings to help identify a
man’s risk of cancer and catch it earlier,” says Houlahan. “Detecting the
disease earlier could significantly improve treatment success.”

This research was supported in part by OICR,
Prostate Cancer Canada, the Terry Fox Research Institute, the Canadian
Institutes for Health Research, the Canadian Cancer Society, the Movember
Foundation and the National Cancer Institute.

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